Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods


Most of the spam filtering techniques are based on objective methods such as the content filtering and DNS/reverse DNS checks. Recently, some cooperative subjective spam filtering techniques are proposed. Objective methods suffer from the false positive and false negative classification. Objective methods based on the content filtering are time consuming and resource demanding. They are inaccurate and require continuous update to cope with newly invented spammer’s tricks. On the other side, the existing subjective proposals have some drawbacks like the attacks from malicious users that make them unreliable and the privacy. In this paper, we propose an efficient spam filtering system that is based on a smart cooperative subjective technique for content filtering in addition to the fastest and the most reliable non-content-based objective methods. The system combines several applications. The first is a web-based system that we have developed based on the proposed technique. A server application having extra features suitable for the enterprises and closed work groups is a second part of the system. Another part is a set of standard web services that allow any existing email server or email client to interact with the system. It allows the email servers to query the system for email filtering. They can also allow the users via the mail user agents to participate in the subjective spam filtering problem.

Share and Cite:

S. A. Elsagheer Mohamed, "Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods," International Journal of Communications, Network and System Sciences, Vol. 6 No. 2, 2013, pp. 88-99. doi: 10.4236/ijcns.2013.62011.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] W.-F. Hsiao and T.-M. Chang, “An Incremental Cluster-Based Approach to SPAM Filtering,” Expert Systems with Applications Journal, Vol. 34, No. 3, 1975, pp. 1-28.
[2] N. Leavitt, “Vendors Fight Spam’s Sudden Rise,” IEEE Computer Magazine, Vol. 40, No. 3, 2007, pp. 16-19.
[3] L. M. Spracklin and L. V. Saxton, “Filtering Spam Using Kolmogorov Complexity Estimates,” The Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops, Niagara Falls, 21-23 May 2007, Vol. 1, pp. 321-328.
[4] Y.-M. Wang and M. Ma, “Strider Search Ranger: Towards an Autonomic Anti-Spam Search Engine,” Fourth International Conference on Autonomic Computing, Jacksonville, 11-15 June 2007, pp. 32-32. doi:10.1109/ICAC.2007.38
[5] M. N. Marsono, M. Watheq El-Kharashi and F. Gebali, “Binary LNS-Based Naive Bayes Inference Engine for Spam Control: Noise Analysis and FPGA Implementation,” IET Computers & Digital Techniques, Vol. 2, No. 1, 2008, pp. 56-62.
[6] K. Saraubon and B. Limthanmaphon, “Fast Effective Botnet Spam Detection,” Fourth International Conference on Computer Sciences and Convergence Information Technology, Seoul, 24-26 November 2009, pp. 1066-1070. doi:10.1109/ICCIT.2009.128
[7] Z. H. Duan, P. Chen, F. Sanchez, Y. F. Dong, M. Stephenson and J. Barker, “Detecting Spam Zombies by Monitoring Outgoing Messages,” IEEE INFOCOM 2009, 19-25 April 2009, pp. 1764-1772.
[8] Z. H. Duan, P. Chen, F. Sanchez, Y. F. Dong, M. Stephenson and J. M. Barker, “Detecting Spam Zombies by Monitoring Outgoing Messages,” IEEE Transactions on Dependable and Secure Computing, Vol. 9, No. 2, 2012, pp. 198-210. doi:10.1109/TDSC.2011.49
[9] E. Levy, “The Making of a Spam Zombie Army: Dissecting the Sobig Worms,” IEEE Security & Privacy Magazine, Vol. 1, No. 4, 2003, pp. 58-59.
[10] Z. Li and H. Y. Shen, “SOAP: A Social Network Aided Personalized and Effective Spam Filter to Clean Your E-Mail Box,” Proceedings of IEEE Infocom, Shanghai, 10-15 April 2011, pp. 1835-1843.
[11] J. A. Zdziarski, “Ending Spam—Bayesian Content Filtering and the Art of Statistical Language Classification,” 5th Edition, No starch Press, San Francisco, 2005.
[12] I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C. D. Spyropoulos and P. Stamatopoulos, “Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach,” Proceedings of the Workshop: Machine Learning and Textual Information Access, 2000, pp. 1-13.
[13] M. Sahami, S. Dumais, D. Heckerman and E. Horvitz, “A Bayesian Approach to Filtering Junk Email,” Learning for Text Categorization—Papers from the AAAI Workshop, 1998, pp. 55-62.
[14] D. Karthika Renuka, T. Hamsapriya, M. Raja Chakkaravarthi and P. Lakshmi Surya, “Spam Classification Based on Supervised Learning Using Machine Learning Techniques,” International Conference on Process Automation, Control and Computing, Coimbatore, 20-22 July 2011, pp. 1-7.
[15] H. Drucker, D. Wu and V. N. Vapnik, “Support Vector Machines for Spam Categorization,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, 1999, pp. 1048-1054. doi:10.1109/72.788645
[16] C.-Y. Tseng and M.-S. Chen, “Incremental SVM Model for Spam Detection on Dynamic Email Social Networks,” International Conference on Computational Science and Engineering, Vancouver, 29-31 August 2009, pp. 128-135.
[17] S. Suwa, N. Yamai, K. Okayama, M. Nakamura, “DNS Resource Record Analysis of URLs in E-Mail Messages for Improving Spam Filtering,” IEEE/IPSJ 11th International Symposium on Applications and the Internet (SAINT), Munich, 18-21 July 2011, pp. 439-444.
[18] A. Khanal, B. S. Motlagh and T. Kocak, “Improving the Efficiency of Spam Filtering through Cache Architecture,” 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Istanbul, 24-26 October 2007, pp. 303-309. doi:10.1109/MASCOTS.2007.27
[19] T. R. Surmacz, “Reliability of E-Mail Delivery in the Era of Spam,” 2nd International Conference on Dependability of Computer Systems, Szklarska, 14-16 June 2007, pp. 198-204.
[20] B. Hoanca, “How Good Are Our Weapons in the Spam Wars? IEEE Technology and Society Magazine, Vol. 25, No. 1, 2006, pp. 22-30.
[21] E. Rabinovitch, “Readers’ Comments on SPAM,” IEEE Communications Magazine, Vol. 40, No. 11, 2002, pp. 20-24.
[22] J. Yan and P. L. Cho, “Enhancing Collaborative Spam Detection with Bloom Filters,” Proceedings of the 22nd Annual Computer Security Applications Conference, Miami Beach, December 2006, pp. 414-428.
[23] “Razor: A Distributed, Collaborative, Spam Detection and Filtering Network,” http://razor.sourceforge.net
[24] S. A. Elsagheer Mohamed, “A Solution for Fighting Spammer’s Resources and Minimizing the Impact of Spam,” International Journal of Communications, Network and System Sciences, Vol. 5, No. 7, 2012, pp. 416-422. doi:10.4236/ijcns.2012.57051
[25] X. Carreras and L. Marquez, “Boosting Trees for Antispam Email Filtering,” Proceedings of 4th Int’ l Conference on Recent Advances in Natural Language Processing, 2001, pp. 58-64.
[26] M. R. Islam, W. L. Zhou and M. U. Choudhury, “Dynamic Feature Selection for Spam Filtering Using Support Vector Machine,” 6th IEEE/ACIS International Conference on Computer and Information Science, Melbourne, 11-13 July 2007, pp. 757-762. doi:10.1109/ICIS.2007.92
[27] W. W. Cohen, “Learning Rules That Classify E-Mail,” Proceedings of AAAI Spring Symposium on Machine Learning in Information Access, Stanford, 25-27 March 1996, pp. 18-25.
[28] X.-L. Pang, Y.-Q. Feng and W. Jiang, “The Compensation Strategy of Unseen Feature Words in Na?ve Bayes Text Classification,” Journal of Harbin Institute of Technology, 2007.
[29] A. Taweesiriwate, B. Manaskasemsak and A. Rungsawang, “Web Spam Detection Using Link-Based Ant Colony Optimization,” IEEE 26th International Conference on Advanced Information Networking and Applications (AINA), Fukuoka-shi, 26-29 March 2012, pp. 868-873.
[30] S. Mohamed, W. Ata and N. Darwish, “A New Technique for Automatic Text Categorization for Arabic Documents,” Proceedings of 5th International Conference on Internet and Information Technology in Modern Organizations, Cario, December 2005.
[31] H. Q. Zuo, X. Li, O. Wu, W. M. Hu and G. Luo, “Image Spam Filtering Using Fourier-Mellin Invariant Features,” IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, 19-24 April 2009, pp. 849-852.
[32] J.-H. Hsia and M.-S. Chen, “Language-Model-Based Detection Cascade for Efficient Classification of Image-Based Spam E-Mail,” IEEE International Conference on Multimedia and Expo, New York, 28 June-3 July 2009, pp. 1182-1185.
[33] B. Biggio, G. Fumera, I. Pillai and F. Roli, “Image Spam Filtering Using Visual Information,” 14th International Conference on Image Analysis and Processing, Modena, 10-14 September 2007, pp. 105-110.
[34] H. B. Aradhye, G. K. Myers and J. A. Herson, “Image Analysis for Categorization of Image-based Spam E-Mail,” Proceedings of the 8th International Conference on Document Analysis and Recognition, Korea, 29 August-1 September 2005, pp. 914-918.
[35] S. A. E. Mohamed and M. M. Tezeghdanti, “Spam Detection and Categorization for Electronic Arabic Messages: Project Final Technical Report,” Technical Report, 2012. http://aspam.asites.org/0Files/TechnicalReport-FinalV3.pdf

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.